We’re Teaching Robots and AI to Design New Drugs | Al is changing Drug discovery | Latest Technology 2020
We’re Teaching Robots and AI to Design New Drugs | Al is changing Drug discovery | Latest Technology 2020
Every year there are thousands of new drugs and development at labs around the world but only a tiny fraction of them make it through human trials never mind final approval and even among those that do get through the majority of new medications are actually just newer versions of existing drugs like cheaper generic versions
But
there's a new hero in town with the potential to help things along with drug
discovery systems based on artificial intelligence (AL) even though the tech is
still growing several new drugs designed with the aid of ai are already in
clinical trials and together with other modern tech like fully automated robot
scientists ai is changing drug.
Synthetic
Drug:
Discovery
in revolutionary ways and it's accelerating the process beyond anything we've
seen before it's only recently that we've been able to create drugs from
scratch some drugs are based on traditional remedies and natural products like
aspirin the first fully synthetic drug the sedative chloral hydrate was
developed in 1869,
Sulfa
Drugs:
But
there was still quite a bit of trial and error involved rather than throwing
spaghetti at the wall to see what sticks it was throwing chemicals at people to
see if they stopped being sick that's how in 1932.
We
got sulfa drugs when researchers at a chemical dye company discovered some of
those dyes could be used to kill dangerous microbes and pure random luck also
did a lot for the early pharmaceutical industry most famously.
if
alexander fleming had been a little more fastidious about keeping his lab
clean we might not have penicillin.
But
all that has changed starting in the latter half of the 20th century we've seen
the rise of rational drug design that means no more spaghetti throwing instead
scientists build drugs from the ground up driven by hypotheses of how they
might work having so much data to rely on can be a double-edged sword though in
fact in a sense you could say we've gotten too good at this the number of
researchers,
And
person hours it takes to investigate every single drug candidate is truly
monumental but now artificial intelligence is turning the tables by helping
to guide and accelerate this process.
It's
helping identify drug candidates we might not have found
otherwise and cutting years off their development ai systems can be given a
general description of what we want to find analyze a bunch of literature and
databases and pick out the best hits for later research this starts right at
the beginning before you even think about how your new drug would work you have
to decide what to design it for you need a target that target is one of the
dominoes in the sequence of things that happens in your body to create a
disease.
It
could be a mutated gene or an enzyme that's working harder than it should
there's usually a huge body of scientific literature devoted to the disease you
want to treat stuff like research articles clinical trial reports and patient
records deciding on a target based on all this information is like looking for
a needle in a haystack and it's a lot to ask of our brains ai can help us see
the big picture and pick out the one really specific thing.
We
needed from that big picture ai systems use a bunch of different technologies
to do that but generally the key is natural language processing natural
language processing is basically what allows your phone's voice assistant to
understand you when you say play something by Tchaikovsky.
If
you're lucky we'll understand that you want to hear any music composed by
tchaikovsky instead of a specific piece he wrote called something now the ai
doesn't really understand what a chikovsky is but by analyzing the
relationships,between the words and the context it can tell that you want to
look for tchaikovsky in the part of its database that holds a list of composers
and the principle is the same with drug discovery,
So
even though ai doesn't understand what a gene is if you instruct it to sort
through the literature on a given disease it can identify the ones that stand
out
Often
in the context of that disease and crucially do they talk about a causal
relationship if so the ai can conclude that that gene is worth a look natural
language processing also means that an ai system doesn't need this info to be
pre-formatted by an army of people you can feed it things written by a human
like scientific articles or medical case reports so ai-based systems can
analyze enormous amounts of data and give us meaningful answers.
And
they're much faster readers than we are so they can go through way more
information and handle way more complexity than humans alone plus they never
need to duck out for coffee this vastly speeds up the process of finding
potential drug targets that we might have found eventually but it also makes it
possible to find completely new targets that we might not have noticed for
example researchers at the u.s company berg grew cancerous and healthy cells
from over a thousand donors in petri dishes they varied the conditions and tracked
the unbelievably complex chemistry that results from cells simply doing their
thing they ended up with trillions of pieces of data from those samples that's
a lot of raw info about what happens in a cancer cell compared to a healthy.
One
which is to say too much the amount of data they had was so gigantic that
without ai it would have effectively been useless but their ai system was able
to analyze this data and identify various molecules that were out of whack in
the cancer cells compared to the healthy ones this suggested.
A
new cancer drug target a molecule called coenzyme q10
and a new drug candidate they developed based on that ai discovery is now in
clinical trials for pancreatic cancer and squamous cell carcinoma.
So
now we know what we want to target with a novel drug which means we need a
novel drug meaning it's time to identify and synthesize chemical compounds that
will hopefully hit that target in just the way we want but how do you choose
what to synthesize even before ai helpers researchers.
Were
able to make educated guesses about what would work by looking at the chemical
features of substances that they already knew could interact with the target
that interaction doesn't have to be beneficial if something sticks to your
target that gives us a clue for how to design something new that'll stick to it
as well even if researchers knew what chemical features they want their drug
candidate to have that may still mean thousands of options,
And
ai systems come to the rescue here as well scientists can tell the ai what
chemical parameters they're looking for and the system will not only find
suitable candidates but cut the list down to those that will potentially work
best the process is kind of similar to things like using ai for image
recognition when you do an image search for cats you may get 99 images of
felines.
And
one gorgeous kitten looking cloud because of those occasional blunders the ai
doesn't actually make the decision scientists still evaluate the results and
the ai system only helps automate and accelerate the grunt work but like that's
really helpful for example by using ai to help choose chemical compounds the
makers of a new candidate drug for obsessive-compulsive disorder were able to
cut down their development cycle from around five years to one and get their
candidate into clinical trials.
Once
you've decided on a drug candidate and synthesized the right chemical it's time
for testing but before human tests and before animal tests researchers start
with simpler assays that means testing the chemical using cultured cells or
cell-free cocktails containing the drug's potential target it used to take
years to do this preliminary testing for a single potential medication,
High
throughput screening:
but
in the 21st century pharmaceutical companies have turned to robotic high
throughput screening which makes it possible to test hundreds of thousands of
compounds in a single day a human might have to pipet the hundreds or
thousands of candidate compounds into one cell culture dish at a time.
EVE
Robot Scientist:
But
a robot can quickly zip through a bunch of them a researcher can just design
the experiment and then check the results like the voltron of modern
pharmaceutical science a bunch of autonomous capabilities can also be combined
into what's called a robot scientist that system uses ai to identify
specific experiments with a lot of potential and then autonomously use lab
equipment to perform these experiments and fine-tune its decisions of where
to go next based on the results for example eve an ai equipped robot scientist
at the university of cambridge has already identified a new potential treatment
for malaria eve first identified a list of compounds that may counteract
malaria then screened them against yeast cells,
And
culture to see which of those chemicals worked best this way the cambridge team
was able to report that eve had identified the well-known antimicrobial
compound triclosan as a candidate to help combat treatment-resistant strains of
malaria even with all this futuristic tech though only a few dozen new drugs
are approved every year for example in 2019 the us food and drug
administration approved 48 but only 20 of them were actually distinct enough
from existing medications to be considered truly.
New
that's because discovering a new drug and bringing it to market is an expensive
long and difficult process to develop one new drug candidate researchers need
to screen up to ten thousand compounds and on average only five of those turn
out to be good enough to go into clinical testing if it gets that far a drug
candidate needs to complete three phases of clinical trials of those ninety
percent fail to gain fda approval and that's why developing a new drug may
take up to 15 years and cost around 1.3 billion dollars each but smart ai
systems.
Like
the ones we've talked about today are in a position to change that we're only
beginning to see the results of this ai revolution in drug discovery there are
literally hundreds of companies developing ai-based systems for the
pharmaceutical industry human scientists still drive the process and while
machines are great at finding patterns and sorting through all of the
information in a hurry our brains can do a whole lot that machines can't but
the help that ai represents is already truly revolutionary it can cut the time
and cost necessary to develop a drug and by doing.
So
it's widening that pipeline of potentially life-saving treatments when
candidate drugs inevitably flunk out because they don't work or aren't safe for
us to use this technology ensures there will be more waiting for us to test and
that's great for all of us even if it means the future looks kind of like a
robot handing us a miracle pill we aren't robots and we don't have any miracle
drugs handy,
But what we do have are pins for sale over at dftba specifically today is your very last chance to grab this month's pin of the month which is of the chandra x-ray observatory it's a very charming little telescope even if it kind of looks like a traffic cone laying on its side either way this pin will only be on sale through midnight tonight september 30th after that we'll have another pin available for pre-order and start shipping this one out to get your hands on it check out the link in the description you
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